Robust lockwire segmentation with multiscale boundary-driven regional stability.

Lockwire segmentation plays a vital role in ensuring mechanical safety in industrial fields. Aiming at the missed detection problem encountered in blurred and low-contrast situations, we propose a robust lockwire segmentation method based on multiscale boundary-driven regional stability. We first design a novel multiscale boundary-driven stability criterion to generate a blur-robustness stability map. Then, the curvilinear structure enhancement metric and linearity measurement function are defined to compute the likeliness of stable regions to belong to lockwires. Finally, the closed boundaries of lockwires are determined to achieve accurate segmentation. Experimental results demonstrate that our proposed method outperforms state-of-the-art object segmentation methods.

A Phase Retrieval Method for 3D Shape Measurement of High-Reflectivity Surface Based on π Phase-Shifting Fringes.

Fringe projection profilometry (FPP) has been widely used for 3D reconstruction, surface measurement, and reverse engineering. However, if the surface of an object has a high reflectivity, overexposure can easily occur. Image saturation caused by overexposure can lead to an incorrect intensity of the captured pattern images, resulting in phase and measurement errors of FPP. To address this issue, we propose a phase retrieval method for the 3D shape measurement of high-reflectivity surfaces based on π phase-shifting fringes. Our method only requires eight images to be projected, including three single-frequency three-step phase-shifting patterns and one pattern used to provide phase unwrapping constraints, called conventional patterns, as well as the π phase-shifting patterns corresponding to the four conventional patterns, called supplemental patterns. Saturated pixels of conventional fringes are replaced by unsaturated pixels in supplemental fringes to suppress phase retrieval errors. We analyzed all 16 replacement cases of fringe patterns and provided calculation methods for unwrapped phases. The main advantages of our method are as follows: (1) By combining the advantages of the stereo phase unwrapping (SPU) algorithm, the number of projected fringes is reduced. (2) By utilizing the phase unwrapping constraint provided by the fourth fringe pattern, the accuracy of SPU is improved. For highly reflective surfaces, the experimental results demonstrate the performance of the proposed method.

Single-Cell Quantitative Phenotyping via the Aptamer-Mounted Nest-PCR (Apt-nPCR).

Analyzing single-cell phenotypes is increasingly required in biomedical studies, for non-genetic understanding of cellular activities and the biological significance of rare cell subpopulations. However, as compared to the genotypic analysis, single-cell phenotype analysis is technically more challenging. Herein, a tractable method that allows quantitative phenotyping of single cell is developed in this work, termed as the aptamer-mounted nest-PCR (Apt-nPCR). In specific, only two rounds of PCR reactions are required to complete the analysis, where aptamers (short oligonucleotides that bind to specific target molecules) are used as the recognition elements to bind antigens and also as the templates of nPCR for multiplexed and quantitative detection. So, quantitative information of these target antigens can be revealed by quantitative PCR analysis of these aptamers, which can thus be used to interpret cell phenotypes in a quantitative-to-qualitative way. By addressing two technical issues that are involved in single-cell phenotype analysis─multiplexed detection plus high sensitivity, we have shown the availability of this method for single-cell phenotyping. Therefore, the Apt-nPCR method may represent a tractable method to facilitate the single-cell phenotype analysis, which can be used as a complementary method against these single-cell genotyping methods in our daily research.

Optimization Model of Signal-to-Noise Ratio for a Typical Polarization Multispectral Imaging Remote Sensor.

The signal-to-noise ratio (SNR) is an important performance evaluation index of polarization spectral imaging remote sensors. The SNR-estimation method based on the existing remote sensor is not perfect. To improve the SNR of this model, a partial detector check slant direction is presented in this study, and a polarization extinction ratio related to the internal SNR model of a typical multispectral imaging remote sensor is combined with the vector radiative transfer model to construct the atmosphere 6SV-SNR coupling model. The new result is that the central wavelength of the detection spectrum, the observation zenith angle, and the extinction ratio all affect the SNR of the remote sensor, and the SNR increases with the increase in the central wavelength of the detection spectrum. It is proved that the model can comprehensively estimate the SNR of a typical polarization multispectral imaging remote sensor under different detection conditions, and it provides an important basis for the application evaluation of such remote sensors.

A Fully Convolutional Network-Based Tube Contour Detection Method Using Multi-Exposure Images.

The tube contours in two-dimensional images are important cues for optical three-dimensional reconstruction. Aiming at the practical problems encountered in the application of tube contour detection under complex background, a fully convolutional network (FCN)-based tube contour detection method is proposed. Multi-exposure (ME) images are captured as the input of FCN in order to get information of tube contours in different dynamic ranges, and the U-Net type architecture is adopted by the FCN to achieve pixel-level dense classification. In addition, we propose a new loss function that can help eliminate the adverse effects caused by the positional deviation and jagged morphology of tube contour labels. Finally, we introduce a new dataset called multi-exposure tube contour dataset (METCD) and a new evaluation metric called dilate inaccuracy at optimal dataset scale (DIA-ODS) to reach an overall evaluation of our proposed method. The experimental results show that the proposed method can effectively improve the integrity and accuracy of tube contour detection in complex scenes.

Identifying multiple line-structured lights from images via a local-to-global graph representation.

Identifying multiple line-structured lights from an image is a fundamental yet challenging issue in the active 3D visual reconstruction. The existing approaches using complex coding schemes are typically time-consuming and inapplicable to real-time sparse 3D reconstruction. In this paper, we solve the multi-line ambiguity from a new viewpoint-distribution pattern of the light segments in the image. We construct a local-to-global graph framework to fully describe the hierarchical distribution of multiple line-structured lights in a 2D image. The lights are firstly grouped as several local graphs according to a light overlapping metric. Then, the hierarchies of the local graphs are unified via the depth of the node, leading to a global graph. The lights in the same level of the global graph come from the same laser plane. The experimental results show the applicability of the proposed algorithm to identify scattered light segments and the robustness to varying sensor poses. We further apply the proposed algorithm to a 3D reconstruction case, achieving a reconstruction precision of 0.025mm. The proposed approach avoids complex auxiliary laser coding and thus is more convenient to conduct.

A Robust Laser Stripe Extraction Method for Structured-Light Vision Sensing.

Environmental sensing is a key technology for the development of unmanned cars, drones and robots. Many vision sensors cannot work normally in an environment with insufficient light, and the cost of using multiline LiDAR is relatively high. In this paper, a novel and inexpensive visual navigation sensor based on structured-light vision is proposed for environment sensing. The main research contents of this project include: First, we propose a laser-stripe-detection neural network (LSDNN) that can eliminate the interference of reflective noise and haze noise and realize the highly robust extraction of laser stripes region. Then we use a gray-gravity approach to extract the center of laser stripe and used structured-light model to reconstruct the point clouds of laser center. Then, we design a single-line structured-light sensor, select the optimal parameters for it and build a car-platform for experimental evaluation. This approach was shown to be effective in our experiments and the experimental results show that this method is more accurate and robust in complex environment.

Camera calibration based on two-cylinder target.

A large-size 2D target cannot be used for calibration in the application scenarios with confined spaces, neither can traditional 1D target because the need of constrained motion. To solve this problem, a camera calibration method based on two-cylinder (TC) target is proposed. The TC target is placed in the Field of View (FOV) arbitrarily and images of the target are acquired from multiple views, then the camera can be calibrated by establishing the perspective projection relationship between the TC target and its projective contours in each view. The experiments with both synthetic and real data show that the proposed method has better anti-noise ability and higher accuracy compared with small-size 2D target.

A high precision 3D reconstruction method for bend tube axis based on binocular stereo vision.

Previous stereo-vision based methods for measuring bent tube axis usually approximate the centerlines of image contours as the axis projection, inevitably resulting in reconstruction errors. This phenomenon is more significant as the tube diameter increases. In this paper, a perspective projection model for any cross section of bent tube was established. Based on this model, a way to locate the precise projected position on image planes of points laying on axis was proposed and 3D coordinates of axis points are reconstructed by binocular stereo vision. We measured three bent tubes with different diameters. Compared with classical approaches, this method effectively reduces the reconstruction errors with measurement accuracy practically independent of diameter.

Dynamic structured-light measurement for wheel diameter based on the cycloid constraint.

Wheel diameter is a significant parameter related to the safety and comfort level of trains. The traditional circle fitting method can be sensitive to noise because of the small distribution of contact points. This paper proposes a structured-light measurement based on the cycloid constraint to obtain a wide distribution of contact points. The wheel is measured twice while running. The cycloid is used to integrate all the contact points to one circle. Simulations are implemented to analyze the possible errors, and the results are compared with the traditional method, which shows the method is more precise and has good stability against 3D reconstruction noise and out-of-roundness. The physical experiment also shows that the result is precise and robust.

Dynamic Measurement for the Diameter of A Train Wheel Based on Structured-Light Vision.

Wheels are very important for the safety of a train. The diameter of the wheel is a significant parameter that needs regular inspection. Traditional methods only use the contact points of the wheel tread to fit the rolling round. However, the wheel tread is easily influenced by peeling or scraping. Meanwhile, the circle fitting algorithm is sensitive to noise when only three points are used. This paper proposes a dynamic measurement method based on structured-light vision. The axle of the wheelset and the tread are both employed. The center of the rolling round is determined by the axle rather than the tread only. Then, the diameter is calculated using the center and the contact points together. Simulations are performed to help design the layout of the sensors, and the influences of different noise sources are also analyzed. Static and field experiments are both performed, and the results show it to be quite stable and accurate.

Global Calibration of Multiple Cameras Based on Sphere Targets.

Global calibration methods for multi-camera system are critical to the accuracy of vision measurement. Proposed in this paper is such a method based on several groups of sphere targets and a precision auxiliary camera. Each camera to be calibrated observes a group of spheres (at least three), while the auxiliary camera observes all the spheres. The global calibration can be achieved after each camera reconstructs the sphere centers in its field of view. In the process of reconstructing a sphere center, a parameter equation is used to describe the sphere projection model. Theoretical analysis and computer simulation are carried out to analyze the factors that affect the calibration accuracy. Simulation results show that the parameter equation can largely improve the reconstruction accuracy. In the experiments, a two-camera system calibrated by our method is used to measure a distance about 578 mm, and the root mean squared error is within 0.14 mm. Furthermore, the experiments indicate that the method has simple operation and good flexibility, especially for the onsite multiple cameras without common field of view.

Dynamic tread wear measurement method for train wheels against vibrations.

Dynamic tread wear measurement is difficult but significant for railway transportation safety and efficiency. The accuracy of existing methods is inclined to be affected by environmental vibrations since they are highly dependent on the accurate calibration of the relative pose between vision sensors. In this paper, we present a method to obtain full wheel profiles based on automatic registration of vision sensor data instead of traditional global calibrations. We adopt two structured light vision sensors to recover the inner and outer profiles of each wheel, and register them by the iterative closest point algorithm. Computer simulations show that the proposed method is insensitive to noises and relative pose vibrations. Static experiments demonstrate that our method has high accuracy and great repeatability. Dynamic experiments show that the measurement accuracy of our method is about 0.18 mm, which is a twofold improvement over traditional methods.

Umbilical cord mesenchymal stem cell transfusion ameliorated hyperglycemia in patients with type 2 diabetes mellitus.

BACKGROUND: Type 2 diabetes mellitus (T2DM) is a serious threat to human health and remains incurable. Insulin deficiency seems to be attributed to the progressive failure of pancreatic islet ß-cells and immune cells such as T cells mediated cytotoxicity may be involved in the loss of pancreatic islet ß-cells in T2DM. Targeting on the immune system to maintain functional activity of pancreatic islet ß-cells could be an attractive way to treat T2DM. Mesenchymal stem cells (MSCs) exert potent capacity of immunomodulation. MSCs have been successfully applied for the treatment of several types of autoimmune diseases. So, the aim of this study is to evaluate the safety and potential therapeutic effects of UMSC on T2DM. METHODS: UMSCs were separated, expanded, and identified on the basis of the previous description. 18 patients of T2DM were recruited according to our experimental protocol. UMSC was intravenously transfused three times. All patients were followed up in the first, third, and sixth month. Age, gender, diabetes duration and medications as well as weight, height, and BMI were recorded. Fasting plasma glucose (FPG), postprandial blood glucose (PBG), HbA1c, C-peptide, and subsets of T cells were measured. All adverse reactions were carefully documented. Effective criteria were made and data was analyzed using SPSS 19.0 software. RESULTS: UMSCs were successful obtained. Baseline clinical characteristics between the efficacy and inefficacy groups were not statistically different (p > 0.05). FBG and PBG of the patients in efficacy group were significantly reduced (p < 0.05) after UMSC transfusion. Plasma C-peptide levels and regulatory T (Treg) cell number in the efficacy group were numerically higher after UMSC transfusion; however, the difference of both parameters did not reach significance (p > 0.05). During the treatment course only 4 out of 18 patients (22.2%) had slight transient fever. Up to 6 months after UMSC transfusion, all patients continued to have a feeling of well-being and were physically more active. CONCLUSIONS: UMSC transfusion is safe and well tolerated, effectively alleviates blood glucose, and increases the generation of C-peptide levels and Tregs in a subgroup of T2DM patients. This pilot study provides fundamental data for further study of UMSC transfusion on control of blood glucose as well as morbidity of T2DM in a larger cohort.

A novel chitosan/cellulose phosphonate composite hydrogel for ultrafast and efficient removal of Pb(II) and Cu(II) from wastewater.

Developing green and high-performance adsorbents to separate heavy metals from wastewater is a challenging task. Biomass hydrogel has the advantages of low cost, renewability, and biodegradability, but it has the problem of low adsorption efficiency. Herein, a novel chitosan/cellulose phosphonate composite hydrogel(CS/MCCP) is fabricated by two steps of reactions including the Phosphorylation reaction and the Mannich reaction. As an excellent chelating group, the phosphonate group greatly enhances the adsorption efficiency of the biomass hydrogel. The CS/MCCP shows ultrafast adsorption rate and excellent adsorption capacity for Pb(II) and Cu(II). The saturated adsorption capacity of Pb(II) and Cu(II) is 211.42 and 74.29 mg·g-1, respectively. The adsorption equilibration time is only 10 min. The adsorption performance of the CS/MCCP is superior to that of the reported cellulose/chitosan hydrogels. Besides, an in-depth analysis of the adsorption mechanism is conducted using X-ray photoelectron spectroscopy(XPS) combined with Density Functional Theory(DFT) calculation. The results reveal that the adsorption mechanism is electrostatic attraction and surface complexation, and there is a synergistic coordination between the phosphonate groups and the amino groups.

Isolation and characterization of dihydrohomoisoflavonoids from Portulaca oleracea L.

Eight pairs of dihydrohomoisoflavonoids (1-8), including four pairs of enantiomeric aglycones [(R,S)-portulacanones B (1) and C (2) and (R,S)-oleracones C (3) and Q (4)] and four pairs of epimeric glycosides [portulacasides A-D and epiportulacasides A-D (5-8)], were obtained from Portulaca oleracea L. Among them, (R,S)-oleracone Q (4) and four pairs of epimeric glycosides (5-8) were reported for the first time. The 50% EtOH fraction from the 70% EtOH extract prevented HepG2 human liver cancer cell damage induced by N-acetyl-p-aminophenol (APAP), and the cell survival rate was 62.3%. Portulacaside B (6a), which was isolated from the 50% EtOH fraction, exhibited hepatoprotective and anti-inflammatory effects. The compound increased the survival rate of APAP-damaged HepG2 human liver cancer cells from 40.0% to 51.2% and reduced nitric oxide production in RAW 264.7 macrophages, resulting in an inhibitory rate of 46.8%.

New phenols and one aminobenzoic acid derivative isolated from the roots of Rhus chinensis Mill.

Four new phenols and one new aminobenzoic acid derivative, with five known phenols were isolated from the roots of Rhus chinensis Mill. Their structures were elucidated by UV, IR, HRESIMS, 1D and 2D NMR spectra, as well as optical rotations. Compound 4 significantly inhibited mouse ear inflammation (inhibitory rate of 44.03%), and significantly extended the time of pain response (extension rate of 48.55%), showing significant anti-inflammatory and analgesic effects in vivo.

High-dynamic angle measurement based on laser displacement sensors.

It is currently difficult to achieve good real-time dynamic angle measurements with high accuracy and large ranges. In this paper, a photoelectric measurement method for dynamic angles based on three laser displacement sensors (LDSs) is proposed. Offline, a dynamic angle vision measurement model is established, and the system is calibrated by using a planar target moved by a 2D moving platform. In the course of measurement, three laser beams emitted from three LDSs are projected onto a rotating plane, and three noncollinear points are acquired synchronously; then the rotation angle is calculated in real time. Simulations verify the feasibility of the method theoretically. Experimental results demonstrate that the method achieves measurement accuracies of 0.008° and 0.046° under quasi-static condition of 80°/s and highly dynamic condition of 1000°/s within the measurement range of about ±40°, respectively.

From entrepreneurship education to entrepreneurial intention: Mindset, motivation, and prior exposure.

We studied the relationships between entrepreneurship education and entrepreneurial intention among college students, with a focus on the mediating role of an entrepreneurial mindset as well as the moderating roles of learning motivation and prior entrepreneurial exposure. More than 90,000 students from 100 colleges or universities participated in the investigation, the data were subjected to structural equation modeling with Mplus. The results indicated that entrepreneurship education (curriculum attendance and extracurricular activity) significantly enhanced the entrepreneurial mindset of students, which, in turn, strengthened their entrepreneurial intention. In terms of learning, intrinsic motivation positively moderated the relationships between curriculum attendance and entrepreneurial intention/mindset, whereas extrinsic motivation moderated it negatively. Entrepreneurial exposure positively moderated the correlation between extracurricular activity and academic performance. Implications concerning the adjustment of entrepreneurship education to the entrepreneurial climate are discussed.

Recent Advances in Light-Conversion Phosphors for Plant Growth and Strategies for the Modulation of Photoluminescence Properties.

The advent of greenhouses greatly promoted the development of modern agriculture, which freed plants from regional and seasonal constraints. In plant growth, light plays a key role in plant photosynthesis. The photosynthesis of plants can selectively absorb light, and different light wavelengths result in different plant growth reactions. Currently, light-conversion films and plant-growth LEDs have become two effective ways to improve the efficiency of plant photosynthesis, among which phosphors are the most critical materials. This review begins with a brief introduction of the effects of light on plant growth and the various techniques for promoting plant growth. Next, we review the up-to-date development of phosphors for plant growth and discussed the luminescence centers commonly used in blue, red and far-red phosphors, as well as their photophysical properties. Then, we summarize the advantages of red and blue composite phosphors and their designing strategies. Finally, we describe several strategies for regulating the spectral position of phosphors, broadening the emission spectrum, and improving quantum efficiency and thermal stability. This review may offer a good reference for researchers improving phosphors to become more suitable for plant growth.